Robust Programming Design

Robust possibilistic programming for socially responsible supply chain network design: A new approach.

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Download files for later. Send to friends and colleagues. Modify, remix, and reuse (just remember to cite OCW as the source.) Learn more. Course Features • • • Course Highlights The course includes a substantial, team-based design project. The goal will be to define industrially relevant design projects in SDM sponsor companies and at MIT. Teams will document their efforts in a written report and make a formal presentation of their results.

Aldo Grasso Storia Della Televisione Italiana Pdf To Jpg on this page. Course Description This course was created for the 'product development' track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development. After taking this course, a student should be able to: • Formulate measures of performance of a system or quality characteristics.

These quality characteristics are to be made robust to noise affecting the system. • Sythesize and select design concepts for robustness. • Identify noise factors whose variation may affect the quality characteristics. • Estimate the robustness of any given design (experimentally and analytically). • Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment). • Select rational tolerances for a design.

• Explain the role of robust design techniques within the wider context of the product development process. • Lead product development activities that include robust design techniques.

In this paper, a bi-objective mixed integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model helps to make several decisions about the strategic issues such as opening of pharmaceutical manufacturing centers and main/local distribution centers along with optimal material flows over a mid-term planning horizon as the tactical decisions.

Jamo Cinemaster Manual on this page. It aims to concurrently minimize the total costs and unfulfilled demands as the first and second objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, a robust possibilistic programming approach is used to handle uncertain parameters.

In order to verify and analyze the proposed model, it is tested on a real case study and managerial insights are provided. • Previous article in issue • Next article in issue.